Individual Puck Possessions Part I
Bibliographic record
Abstract
In this paper we use puck and player tracking data from the 2023- 24 NHL season to study individual player possessions (focusing on 5v5 situations). We study metrics such as possession count, average and total possession duration, average and total distance travelled with the puck, and examine relationships between these metrics and traditional measures of success (i.e., goals, assists and points). A key finding is that individual offensive zone possession is strongly correlated with points (r = 0.70) and is moderately correlated with goals (r = 0.64), assists (r = 0.54), and shots on goal (r = 0.69). We also observe differences in individual possessions based on position (forwards versus defence), zone of play, and strength and large and statistically significant differences between top ranked players and league averages (across most possession metrics). Finally, we examine the benefits of our individual possession metrics and find that they are highly stable (so they are useful for predictions), able to differentiate players, and provide information not captured by existing metrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".